hsinyinglee / mdmm Goto Github PK
View Code? Open in Web Editor NEWLearning multi-domain multi-modality I2I translation
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jdc08161063 qhanson xujinglin shaoguowen lslrh winwinjjiang cv-ip wbteng9526 edmontdants anqw chenhui2333 berther huiwenluo ike-taku kouheifurukawa shuai-xie gboduljakmdmm's Issues
som question about the code
Hi,
I like this work very much and I think it is really cool. But when I tried to use an image in the target domain to decide the attribute of the results, I found the function “test_forward_transfer(self, image, image_trg, c_trg)” in the file named "model.py" requires two arguments "image_trg" and "c_trg", but instead of using these two arguments, the function use "self.image_trg" and "self.c_trg", which wasn't defined in corresponding class "MD_multi()". I don't kown how to trackle this problem, so I take the liberty to ask you for help.
Thanks
Corresponding paper?
Wow, a great work, I wonder there is a corresponding paper about MDMM?
normalization absence
Hello!
I found a following thing in LeakyReLUConv2d:
class LeakyReLUConv2d(nn.Module):
def __init__(self, ..., norm='None', ...):
....
if 'norm' == 'Instance':
model += [nn.InstanceNorm2d(n_out, affine=False)]
...
https://github.com/HsinYingLee/MDMM/blob/master/networks.py#L362
It seems that normalization is never applied in LeakyReLUConv2d block.
Does it affect the model performance, as LeakyReLUConv2d present in MultiDomain Encoder and Discriminators?
Are the best results reported in paper are gained with turned on InstanceNormalization?
Best Regards,
Aleksei Silvestrov
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